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Computer Science

D-Index
33
Citations
16080
World Ranking
12345
National Ranking
4996

Overview

Jasper Snoek is affiliated with Google in the United States and works primarily within the field of Computer Science. Their research output includes 78 publications, with a strong focus on Artificial Intelligence, which accounts for 62 of those works. Other subfields contributing to their research portfolio include Computer Vision and Pattern Recognition, Control and Systems Engineering, Computational Theory and Mathematics, and Management Science and Operations Research.

The scientist's research addresses various specialized topics related to machine learning and its applications. Major topics of study include:

  • Adversarial Robustness in Machine Learning
  • Gaussian Processes and Bayesian Inference
  • Machine Learning and Data Classification
  • Anomaly Detection Techniques and Applications
  • Advanced Neural Network Applications
  • Domain Adaptation and Few-Shot Learning
  • Fault Detection and Control Systems

Jasper Snoek has collaborated frequently with several researchers. Notable frequent coauthors include Balaji Lakshminarayanan, Rodolphe Jenatton, Zachary Nado, Dustin Tran, and Zelda Mariet.

The scientist has contributed to multiple publication venues with a particular emphasis on the arXiv repository, where 30 papers have been published. Other venues include Entropy and npj Digital Medicine.

Recent papers authored or coauthored by Jasper Snoek demonstrate ongoing engagement with topics in medical machine learning, robust model training, ensemble methods, and uncertainty quantification. Selected recent publications are:

  • "Second opinion needed: communicating uncertainty in medical machine learning," 2021, npj Digital Medicine
  • "Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift," 2020, arXiv (Cornell University)
  • "Plex: Towards Reliability using Pretrained Large Model Extensions," 2022, arXiv (Cornell University)
  • "A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness," 2022, arXiv (Cornell University)
  • "Hydra: Preserving Ensemble Diversity for Model Distillation," 2020, arXiv (Cornell University)

Best Publications

  • Practical Bayesian Optimization of Machine Learning Algorithms

    Jasper Snoek;Hugo Larochelle;Ryan P Adams

  • Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

    David R. Kelley;Jasper Snoek;John L. Rinn

  • Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift

    Yaniv Ovadia;Emily Fertig;Jie Ren;Zachary Nado

  • Scalable Bayesian Optimization Using Deep Neural Networks

    Jasper Snoek;Oren Rippel;Oren Rippel;Kevin Swersky;Ryan Kiros

  • Multi-Task Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan P Adams

  • Sequential regulatory activity prediction across chromosomes with convolutional neural networks

    David R. Kelley;Yakir A. Reshef;Maxwell Bileschi;David Belanger

  • Bayesian optimization with unknown constraints

    Michael A. Gelbart;Jasper Snoek;Ryan P. Adams

  • Likelihood Ratios for Out-of-Distribution Detection

    Jie Ren;Peter J. Liu;Emily Amanda Fertig;Jasper Roland Snoek

  • Second opinion needed: communicating uncertainty in medical machine learning

    Benjamin Kompa;Jasper Snoek;Andrew L. Beam

  • Spectral representations for convolutional neural networks

    Oren Rippel;Jasper Snoek;Ryan P. Adams

  • Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling

    Carlos Riquelme;George Tucker;Jasper Roland Snoek

  • Input Warping for Bayesian Optimization of Non-Stationary Functions

    Jasper Snoek;Kevin Swersky;Rich Zemel;Ryan Adams

  • Freeze-Thaw Bayesian Optimization

    Kevin Swersky;Jasper Snoek;Ryan Prescott Adams

  • Learning Latent Permutations with Gumbel-Sinkhorn Networks

    Gonzalo E. Mena;David Belanger;Scott W. Linderman;Jasper Snoek

  • How Good is the Bayes Posterior in Deep Neural Networks Really

    Florian Wenzel;Kevin Roth;Bastiaan Veeling;Jakub Swiatkowski

  • Machine Learning Approaches in Cardiovascular Imaging.

    Mir Henglin;Gillian Stein;Pavel V. Hushcha;Jasper Snoek

  • Winner's Curse? On Pace, Progress, and Empirical Rigor.

    D. Sculley;Jasper Snoek;Alexander B. Wiltschko;Ali Rahimi

  • Hyperparameter Ensembles for Robustness and Uncertainty Quantification

    Florian Wenzel;Jasper Snoek;Dustin Tran;Rodolphe Jenatton

  • How Good is the Bayes Posterior in Deep Neural Networks Really

    Florian Wenzel;Kevin Roth;Bastiaan S. Veeling;Jakub Świątkowski

  • Plex: Towards Reliability using Pretrained Large Model Extensions

    Unknown

  • Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

    Zachary Nado;Shreyas Padhy;D. Sculley;Alexander D'Amour

Frequent Co-Authors

Ryan P. Adams
Ryan P. Adams Princeton University
Balaji Lakshminarayanan
Balaji Lakshminarayanan Google (United States)
Dustin Tran
Dustin Tran Google (United States)
Alex Mihailidis
Alex Mihailidis University of Toronto
Kevin Swersky
Kevin Swersky Google (United States)
Sebastian Nowozin
Sebastian Nowozin Microsoft (United States)
D. Sculley
D. Sculley Google (United States)
Hugo Larochelle
Hugo Larochelle Google (United States)
Richard S. Zemel
Richard S. Zemel University of Toronto
George Tucker
George Tucker Google (United States)

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